# 11 - Transfer Learning and Fine-Tuning: Reusing Pre-trained Models

Accelerate ML: reuse pre-trained models with transfer learning. Learn when it makes sense, how fine-tuning works, layer freezing vs unfreezing, data augmentation. Use cases: image classification, NLP, when it\

## What you'll learn

- Transfer learning reuses models pre-trained on large datasets for new problems
- Feature extraction: freeze the backbone, train only the new classification head
- Fine-tuning: progressively unfreeze layers with a low learning rate
- Works best when source and target domains are similar and target dataset is small
- ResNet/EfficientNet for images, BERT/GPT for text are the standard models

*This article is part of the **Machine Learning** series on federicocalo.dev.*

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## Read the full article

The complete article (15 min read) with code examples, diagrams, and practical exercises is available here:

**➡️ [11 - Transfer Learning and Fine-Tuning: Reusing Pre-trained Models](https://federicocalo.dev/en/blog/transfer-learning-fine-tuning-pretrained-models)**

`https://federicocalo.dev/en/blog/transfer-learning-fine-tuning-pretrained-models`

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*By [Federico Calò](https://federicocalo.dev) — Software Developer & Technical Writer*